Insights from the Climate-Smart Agriculture Policy Simulator

A new tool created for Climate Interactive’s Climate-Smart Agriculture project has shown some of the ways agriculture will have to transform in the face of climate change. We have found that agriculture can be a part of the solution for development as well as for climate mitigation and adaptation – as long as agricultural policies follow certain principles. Countries will need to heed the following insights to bring about a sustainable food system that produces a valuable and secure supply of food, on healthy land, with lower greenhouse gas emissions:

Stop growth: Counter the long-term growth of farmland and livestock

Do more with less: Foster efficiency and improve yields using fewer inputs

Emit less: Reduce emissions intensity of farmland and livestock

Control demand: Stabilize population size and living standards in the long run

For Africa in particular, policymakers need to apply these principles to agricultural policies as they develop their economies and improve food availability. The policy needs are real: Africa will be more food insecure because of impending climatic changes. Policymakers need to see and understand the trade-offs between different solutions as they respond to these challenges.

Our first major version of the Climate and Agriculture Policy Simulator is calibrated to Ethiopia and examines their Climate Resilient Green Economy Plan (CRGE, Ethiopia 2011). Historical data are obtained from the FAO (FAOSTAT 2017) or Ethiopia’s National Communication to the UNFCCC (Ethiopia 2015b). The policies the user can adopt are based on proposals found in Ethiopia’s strategic documents (Ethiopia 2011, 2015a, 2016).

The choice of Ethiopia was not meant to endorse or critique their policies in particular. We set out to learn lessons from the expertise and mental models embodied in these proposals, rather than to evaluate the policy per se.

The pictures below show some of the output screens from the simulation, showing the reference or business-as-usual case. Each view has two graphs plus a selection of input controls. All graphs show the year along the horizontal axis and one or more important variables vertically.

The first two views show how the simulation reproduces historical data. Each graph shows model output in bold overlaid with data. The output in terms of food production and greenhouse gas emissions (top) are the result of trends in land use (bottom) and other factors. Were these trends to continue, the results would be as shown below. The food available grows in excess of both basic needs and demand (top) but at the cost of missing other goals. We can see that emissions trends have already made progress compared to what Ethiopia predicted would be their business-as-usual, but their emissions would still be far in excess of their goals, and land use trends would result in ever-shrinking areas of forest and other lands.

These trends are not sustainable and not in line with the desires Ethiopia has expressed. However, Ethiopia has put forward policies to change the trends. The results of successful implementation of those policies are shown below.

Here, the growth in agricultural land and livestock – and the resulting deforestation – halts by 2025. Improvements in crop and livestock practices raise yield enough food to compensate, along with improvements to the value chain which reduce losses. Further improvements such as tillage, erosion, and manure management also lower emissions. The result is that available food grows more quickly in the short term, and remains well above needs and desires. Emissions change more slowly than the goal, but are below the target by 2030.

Lessons Learned

In order to reach this outcome we had to simulate a successful transformation throughout the agricultural system. Our trials taught us several key lessons:

It is vital to address the long-term growth trends in the scale of agriculture. It is not enough to have a temporary pause or reversal of growth if the base trend returns afterwards. All successful scenarios have permanent stabilization in the number of livestock and area of cropland.

The environmental impact of any activity equals the volume of the activity times the activity’s intensity – and both are important. In agriculture, volume is the number of livestock and the hectares of cropland; intensity is the emissions per head of livestock or hectare of land. The highest leverage interventions include reducing both. There is no way to keep lowering emissions while land in agriculture keeps increasing, but lowering emissions factors as much as possible allows sufficient land to remain in use.

Increases to efficiency and yield allow a country to meet its food needs with less land and livestock. The amount of food available equals the crop area or livestock number, times yield, minus losses. Yield improvements, especially if they can be done with low environmental impact, are the key to slowing growth so that other goals can be met. It will be important to assure that gains in yield lead to slower growth rather than ever-increasing surplus and consumption.

If demand grows indefinitely it will overcome any gains. There are probably upper limits to yield, or at least the rate at which yield can improve. There is definitely a lower limit at zero losses – and a non-zero amount of emissions per hectare farmed. After all possible improvements have been made, we still have to get control of growing demand.

The scenario we simulated may best be described as ‘good enough’ through midcentury – emissions start to increase again after 2040. This outcome is a good base to build upon, but there will need to be other strategies later on.

These lessons show that there is reason for grounded hope. It is possible for farmer well-being to improve without development as usual, and for developing countries to leapfrog the path taken by developed ones. Food security is not at odds with environmental quality. There are solutions that are worth the effort, although they may not be easy.

Model development and further details will improve our understanding of what it really takes to achieve success. If you have any questions regarding this work or helping with further refinements to the simulator, contact Travis Franck.